Multichannel active noise control (ANC) systems have been widely investigated for low-frequency noise attenuation over a spatial region. Using a conventional centralized control strategy based on the multichannel filtered- x least mean square (F x LMS) algorithm has been demonstrated to be effective for multichannel ANC systems, but the high computational burden restricts its practical applications. Meanwhile, a decentralized control strategy suffers from stability problems although it has been successful in reducing the computational load. Recently, distributed control strategies, such as the multitask diffusion adaptation scheme, have been introduced to ANC systems and shown to mitigate the stability problems in decentralized systems. However, distributed ANC systems using the diffusion F x LMS algorithm require strong symmetry of acoustic paths because of the dependence on node-based adaptation and neighborhood-based combination. To overcome this limitation, this paper proposes an Augmented Diffusion F x LMS algorithm with neighborhood-based adaptation and node-based combination. A theoretical formulation and convergence analysis are presented and simulations are performed to compare the proposed algorithm with existing ones under different system configurations for tonal, multi-tonal, narrowband and broadband signals. Simulation results demonstrate that the proposed algorithm has the same noise reduction performance as centralized method even if the acoustic paths are strongly asymmetrical, which is superior over existing distributed multitask diffusion strategy.

Accelerating beams have attracted considerable research interest due to their peculiar properties and various applications. Although there have been numerous research on the generation and application of accelerating light beams, few results have been published on the generation of accelerating acoustic beams. Here we report on the experimental observation of accelerating acoustic beams along arbitrary convex trajectories. The desired trajectory is projected to the spatial phase profile on the boundary which is discretized and sampled spatially. The sound field distribution is formulated with the Green function and the integral equation method. Both the paraxial and the non-paraxial regimes are examined and observed in the experiments. The effect of obstacle scattering in the sound field is also investigated and the results demonstrate that the approach is robust against obstacle scattering. The realization of accelerating acoustic beams will have an impact on various applications where acoustic information and energy are required to be delivered along an arbitrary convex trajectory.

Deep neural network (DNN) based speech enhancement models have attracted extensive attention due to their promising performance. However, it is difficult to deploy a powerful DNN in real-time applications because of its high computational cost. Typical compression methods such as pruning and quantization do not make good use of the data characteristics. In this paper, we introduce the Skip-RNN strategy into speech enhancement models with parallel RNNs. The states of the RNNs update intermittently without interrupting the update of the output mask, which leads to significant reduction of computational load without evident audio artifacts. To better leverage the difference between the voice and the noise, we further regularize the skipping strategy with voice activity detection (VAD) guidance, saving more computational load. Experiments on a high-performance speech enhancement model, dual-path convolutional recurrent network (DPCRN), show the superiority of our strategy over strategies like network pruning or directly training a smaller model. We also validate the generalization of the proposed strategy on two other competitive speech enhancement models.

Recent studies on diffusion adaptation for distributed active noise control (DANC) systems have attracted significant research interest due to their balance between computational burden and stability compared to conventional centralized and decentralized adaptation schemes. The conventional multitask diffusion FxLMS algorithm assumes that the converged solutions of all control filters are consistent to each other, which is unrealistic in practice hence results in inferior performance in noise reduction. An augmented diffusion FxLMS algorithm has been proposed to overcome this problem, which adopts a neighborhood-wide adaptation and node-based combination approach to mitigate the bias in the converged solution of the multitask diffusion algorithms. However, the improvement comes at the expense of a higher computational burden and communication cost. All existing DANC systems, including the multitask and augmented diffusion algorithms, assume one-way communication between nodes. By contrast, this paper proposes a bidirectional communication scheme for the augmented diffusion algorithm to further reduce the memory requirement, computational burden, and communication cost. Simulation results in the free field and with measured room impulse responses both demonstrate that the proposed augmented diffusion algorithm with bidirectional communication can achieve a faster convergence speed than that based on one-way communication with a lower memory, computation, and communication burden.

In this paper, the audio sound field generated by a focusing parametric array loudspeaker (PAL) is investigated both numerically and experimentally. A computationally efficient method is developed to calculate the quasilinear solution of both the Westervelt and Kuznetsov equations. The accuracy of generated sound field using Westervelt equation is initially examined, and the results are then compared with those obtained using the Kuznetsov equation. It is found that the focusing on the true focal location deteriorates the prediction accuracy using the Westervelt equation due to the strong and complicated local effects. To validate the numerical results against experimental data, a 37-channel focusing PAL prototype is constructed by using a field programmable gate array, metal-oxide semiconductor field-effect transistor drivers and the pulse train signal technique. Both the simulation and experimental results show that the audio sound pressure around the true focal location is increased, and the increment becomes larger at low audio frequencies and when the focal point moves close to the radiation surface, which is attributed to the strong local effects in the near field. It has been shown that a focusing PAL can improve the poor low frequency response for a conventional PAL without focusing, and the generated audio sound decays rapidly with the distance in the far field when compared to that without focusing.

In some situations of active noise control, infinite impulse response (IIR) filters are more suitable than finite impulse response (FIR) filters owing to the poles in the transfer function. A number of algorithms have been derived for applying IIR filters in active noise control; however, most of them use the direct form IIR filter structure, which faces the difficulties of checking stability and relatively slow convergence speed for noise composed of narrow-band components with large power disparity. To overcome these difficulties along with using the direct form IIR filters, a new adaptive algorithm is proposed in this paper, which uses and updates the lattice form adaptive IIR filter in an active noise control system. Full mathematical derivations of the proposed algorithm are presented, and the comparison between the proposed algorithm and the commonly used filtered-u LMS and filtered-v LMS algorithms shows the superiority of the proposed algorithm.